Logics for the Semantic Web
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Metadata for Semantic and Social Applications
etadata is a key aspect of our evolving infrastructure for information management, social computing, and scientific collaboration. DC-2008M will focus on metadata challenges, solutions, and innovation in initiatives and activities underlying semantic and social applications. Metadata is part of the fabric of social computing, which includes the use of wikis, blogs, and tagging for collaboration and participation. Metadata also underlies the development of semantic applications, and the Semantic Web — the representation and integration of multimedia knowledge structures on the basis of semantic models. These two trends flow together in applications such as Wikipedia, where authors collectively create structured information that can be extracted and used to enhance access to and use of information sources. Recent discussion has focused on how existing bibliographic standards can be expressed as Semantic Metadata for Web vocabularies to facilitate the ingration of library and cultural heritage data with other types of data. Harnessing the efforts of content providers and end-users to link, tag, edit, and describe their Semantic and information in interoperable ways (”participatory metadata”) is a key step towards providing knowledge environments that are scalable, self-correcting, and evolvable. Social Applications DC-2008 will explore conceptual and practical issues in the development and deployment of semantic and social applications to meet the needs of specific communities of practice. Edited by Jane Greenberg and Wolfgang Klas DC-2008 -
From the Semantic Web to Social Machines
ARTICLE IN PRESS ARTINT:2455 JID:ARTINT AID:2455 /REV [m3G; v 1.23; Prn:25/11/2009; 12:36] P.1 (1-6) Artificial Intelligence ••• (••••) •••–••• Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint From the Semantic Web to social machines: A research challenge for AI on the World Wide Web ∗ Jim Hendler a, , Tim Berners-Lee b a Tetherless World Constellation, RPI, United States b Computer Science and AI Laboratory, MIT, United States article info abstract Article history: The advent of social computing on the Web has led to a new generation of Web Received 24 September 2009 applications that are powerful and world-changing. However, we argue that we are just Received in revised form 1 October 2009 at the beginning of this age of “social machines” and that their continued evolution and Accepted 1 October 2009 growth requires the cooperation of Web and AI researchers. In this paper, we show how Available online xxxx the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research. © 2009 Elsevier B.V. All rights reserved. Much has been written about the profound impact that the World Wide Web has had on society. Yet it is primarily in the past few years, as more interactive “read/write” technologies (e.g. Wikis, blogs and photo/video sharing) and social network- ing sites have proliferated, that the truly profound nature of this change is being felt. From the very beginning, however, the Web was designed to create a network of humans changing society empowered using this shared infrastructure. -
V a Lida T in G R D F Da
Series ISSN: 2160-4711 LABRA GAYO • ET AL GAYO LABRA Series Editors: Ying Ding, Indiana University Paul Groth, Elsevier Labs Validating RDF Data Jose Emilio Labra Gayo, University of Oviedo Eric Prud’hommeaux, W3C/MIT and Micelio Iovka Boneva, University of Lille Dimitris Kontokostas, University of Leipzig VALIDATING RDF DATA This book describes two technologies for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL), the rationales for their designs, a comparison of the two, and some example applications. RDF and Linked Data have broad applicability across many fields, from aircraft manufacturing to zoology. Requirements for detecting bad data differ across communities, fields, and tasks, but nearly all involve some form of data validation. This book introduces data validation and describes its practical use in day-to-day data exchange. The Semantic Web offers a bold, new take on how to organize, distribute, index, and share data. Using Web addresses (URIs) as identifiers for data elements enables the construction of distributed databases on a global scale. Like the Web, the Semantic Web is heralded as an information revolution, and also like the Web, it is encumbered by data quality issues. The quality of Semantic Web data is compromised by the lack of resources for data curation, for maintenance, and for developing globally applicable data models. At the enterprise scale, these problems have conventional solutions. Master data management provides an enterprise-wide vocabulary, while constraint languages capture and enforce data structures. Filling a need long recognized by Semantic Web users, shapes languages provide models and vocabularies for expressing such structural constraints. -
Semantics Developer's Guide
MarkLogic Server Semantic Graph Developer’s Guide 2 MarkLogic 10 May, 2019 Last Revised: 10.0-8, October, 2021 Copyright © 2021 MarkLogic Corporation. All rights reserved. MarkLogic Server MarkLogic 10—May, 2019 Semantic Graph Developer’s Guide—Page 2 MarkLogic Server Table of Contents Table of Contents Semantic Graph Developer’s Guide 1.0 Introduction to Semantic Graphs in MarkLogic ..........................................11 1.1 Terminology ..........................................................................................................12 1.2 Linked Open Data .................................................................................................13 1.3 RDF Implementation in MarkLogic .....................................................................14 1.3.1 Using RDF in MarkLogic .........................................................................15 1.3.1.1 Storing RDF Triples in MarkLogic ...........................................17 1.3.1.2 Querying Triples .......................................................................18 1.3.2 RDF Data Model .......................................................................................20 1.3.3 Blank Node Identifiers ..............................................................................21 1.3.4 RDF Datatypes ..........................................................................................21 1.3.5 IRIs and Prefixes .......................................................................................22 1.3.5.1 IRIs ............................................................................................22 -
A New Generation Digital Content Service for Cultural Heritage Institutions
A New Generation Digital Content Service for Cultural Heritage Institutions Pierfrancesco Bellini, Ivan Bruno, Daniele Cenni, Paolo Nesi, Michela Paolucci, and Marco Serena Distributed Systems and Internet Technology Lab, DISIT, Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, Italy [email protected] http://www.disit.dsi.unifi.it Abstract. The evolution of semantic technology and related impact on internet services and solutions, such as social media, mobile technologies, etc., have de- termined a strong evolution in digital content services. Traditional content based online services are leaving the space to a new generation of solutions. In this paper, the experience of one of those new generation digital content service is presented, namely ECLAP (European Collected Library of Artistic Perform- ance, http://www.eclap.eu). It has been partially founded by the European Commission and includes/aggregates more than 35 international institutions. ECLAP provides services and tools for content management and user network- ing. They are based on a set of newly researched technologies and features in the area of semantic computing technologies capable of mining and establishing relationships among content elements, concepts and users. On this regard, ECLAP is a place in which these new solutions are made available for inter- ested institutions. Keywords: best practice network, semantic computing, recommendations, automated content management, content aggregation, social media. 1 Introduction Traditional library services in which the users can access to content by searching and browsing on-line catalogues obtaining lists of references and sporadically digital items (documents, images, etc.) are part of our history. With the introduction of web2.0/3.0, and thus of data mining and semantic computing, including social media and mobile technologies most of the digital libraries and museum services became rapidly obsolete and were constrained to rapidly change. -
Enterprise Data Classification Using Semantic Web Technologies
Enterprise Data Classification Using Semantic Web Technologies David Ben-David1, Tamar Domany2, and Abigail Tarem2 1 Technion – Israel Institute of Technology, Haifa 32000, Israel [email protected] 2 IBM Research – Haifa, University Campus, Haifa 31905, Israel {tamar,abigailt}@il.ibm.com Abstract. Organizations today collect and store large amounts of data in various formats and locations. However they are sometimes required to locate all instances of a certain type of data. Good data classification allows marking enterprise data in a way that enables quick and efficient retrieval of information when needed. We introduce a generic, automatic classification method that exploits Semantic Web technologies to assist in several phases in the classification process; defining the classification requirements, performing the classification and representing the results. Using Semantic Web technologies enables flexible and extensible con- figuration, centralized management and uniform results. This approach creates general and maintainable classifications, and enables applying se- mantic queries, rule languages and inference on the results. Keywords: Semantic Techniques, RDF, Classification, modeling. 1 Introduction Organizations today collect and store large amounts of data in various formats and locations. The data is then consumed in many places, sometimes copied or cached several times, causing valuable and sensitive business information to be scattered across many enterprise data stores. When an organization is required to meet certain legal or regulatory requirements, for instance to comply with regulations or perform discovery during civil litigation, it becomes necessary to find all the places where the required data is located. For example, if in order to comply with a privacy regulation an organization is required to mask all Social Security Numbers (SSN) when delivering personal information to unauthorized entities, all the occurrences of SSN must be found. -
15 Years of Semantic Web: an Incomplete Survey
Ku¨nstl Intell (2016) 30:117–130 DOI 10.1007/s13218-016-0424-1 TECHNICAL CONTRIBUTION 15 Years of Semantic Web: An Incomplete Survey 1 2 Birte Glimm • Heiner Stuckenschmidt Received: 7 November 2015 / Accepted: 5 January 2016 / Published online: 23 January 2016 Ó Springer-Verlag Berlin Heidelberg 2016 Abstract It has been 15 years since the first publications today’s Web: Uniform Resource Identifiers (URIs) for proposed the use of ontologies as a basis for defining assigning unique identifiers to resources, the HyperText information semantics on the Web starting what today is Markup Language (HTML) for specifying the formatting known as the Semantic Web Research Community. This of Web pages, and the Hypertext Transfer Protocol (HTTP) work undoubtedly had a significant influence on AI as a that allows for the retrieval of linked resources from across field and in particular the knowledge representation and the Web. Typically, the markup of standard Web pages Reasoning Community that quickly identified new chal- describes only the formatting and, hence, Web pages and lenges and opportunities in using Description Logics in a the navigation between them using hyperlinks is targeted practical setting. In this survey article, we will try to give towards human users (cf. Fig. 1). an overview of the developments the field has gone through In 2001, Tim Berners-Lee, James Hendler, and Ora in these 15 years. We will look at three different aspects: Lassila describe their vision for a Semantic Web [5]: the evolution of Semantic Web Language Standards, the The Semantic Web is not a separate Web but an evolution of central topics in the Semantic Web Commu- extension of the current one, in which information is nity and the evolution of the research methodology. -
Web Semantics in Cloud Computing
International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 ISSN 2229-5518 378 Review: Web Semantics in Cloud Computing Rupali A. Meshram, Assistant professor, CSE Department, PRMIT&R, Badnera Komal R. Hole, Assistant professor, CSE Department, PRMIT&R, Badnera Pranita P. Deshmukh, Assistant professor, CSE Department, PRMIT&R, Badnera Roshan A. karwa, Assistant professor, CSE Department, PRMIT&R, Badnera Abstract – on web service and the web services are more efficient This paper shows how cloud computing on the so as to attract more users. background of Semantic Web is going to important. Many cloud computing services are Cloud computing has become interesting area both in implementing on distributed web environment and academia and industry. Semantic Web has been an provides virtualized resources, which focuses on some important research area in academic and industrial aspects like the semantic ambiguity, distributed researchers. Many applications will need to work with semantic reasoning, distributed semantic models large amounts of data, one particular application would construction, distributed metadata storage, parallel certainly not exist without the capability of accessing and semantic computing, etc. It also introduces many real processing arbitrary amounts of metadata: search world applications like semantic analysis in science engines that locate the data and services that other computing, cloud resource discovery, accurate applications need. To solve this problem, semantic web advertise recommendation, and web context services used with cloud computing. In this paper we understanding in commercial services. present the use of well established semantic technologies The Semantic Web is gaining immense in Cloud computing. The purpose of this report is to give popularity. -
A Multimodal Approach for Automatic WS Semantic Annotation
MATAWS: A Multimodal Approach for Automatic WS Semantic Annotation Cihan Aksoy1,2, Vincent Labatut1, Chantal Cherifi1,3 and Jean-François Santucci3, 1 Galatasaray University, Computer Science Department, Ortaköy/İstanbul, Turkey 2 TÜBİTAK, Gebze/Kocaeli, Turkey 3 University of Corsica, Corte, France [email protected] Abstract. Many recent works aim at developing methods and tools for the processing of semantic Web services. In order to be properly tested, these tools must be applied to an appropriate benchmark, taking the form of a collection of semantic WS descriptions. However, all of the existing publicly available collections are limited by their size or their realism (use of randomly generated or resampled descriptions). Larger and realistic syntactic (WSDL) collections exist, but their semantic annotation requires a certain level of automation, due to the number of operations to be processed. In this article, we propose a fully automatic method to semantically annotate such large WS collections. Our approach is multimodal, in the sense it takes advantage of the latent semantics present not only in the parameter names, but also in the type names and structures. Concept-to-word association is performed by using Sigma, a mapping of WordNet to the SUMO ontology. After having described in details our annotation method, we apply it to the larger collection of real-world syntactic WS descriptions we could find, and assess its efficiency. Keywords: Web Service, Semantic Web, Semantic Annotation, Ontology, WSDL, OWL-S. 1 Introduction The semantic Web encompasses technologies which can make possible the generation of the kind of intelligent documents imagined ten years ago [1]. -
Semantic Computing
SEMANTIC COMPUTING Lecture 12: Ontology Learning: Introduction Dagmar Gromann International Center For Computational Logic TU Dresden, 19 December 2018 Overview • Defining ontology • Introduction to ontology learning Dagmar Gromann, 19 December 2018 Semantic Computing 2 What is an ontology? Dagmar Gromann, 19 December 2018 Semantic Computing 3 What is an ontology? • Ontology (no plural = uncountable): philosophical study of being (existence, reality, categories of beings, etc.) • ontology (ontologies = countable): “formal, explicit specification of shared conceptualizations.’ (Studer et al. 1998: 186) • computational artifact designed with a purpose in mind • represented in a formal language to allow for the processing, reusing, and sharing of knowledge among humans and machines Studer, Rudi, Benjamins, Richard V., and Fensel, Dieter (1998), ’Knowledge Engineering: Principles and Methods’, Data & Knowledge Engineering, 25 (1-2), 161-198. Dagmar Gromann, 19 December 2018 Semantic Computing 4 Specification? Formal? A formal, explicit specification of shared conceptualizations; ideally an ontology: • is a model of (some aspect of) the world • captures a shared understanding of the domain of interest, a shared conceptualization • defines a vocabulary relevant to the domain and interpreted the same way by different users • specifies the meaning of the vocabulary in an explicit manner and often in a formal specification language Two main parts: • structure of the model = set of axioms • particular objects and situations = set of instances Dagmar Gromann, -
Exploiting Semantic Web Knowledge Graphs in Data Mining
Exploiting Semantic Web Knowledge Graphs in Data Mining Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim presented by Petar Ristoski Mannheim, 2017 ii Dekan: Dr. Bernd Lübcke, Universität Mannheim Referent: Professor Dr. Heiko Paulheim, Universität Mannheim Korreferent: Professor Dr. Simone Paolo Ponzetto, Universität Mannheim Tag der mündlichen Prüfung: 15 Januar 2018 Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many in- formation systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations be- tween them, which can be utilized in various natural language processing, infor- mation retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. -
Semantic Data Mining: a Survey of Ontology-Based Approaches
Semantic Data Mining: A Survey of Ontology-based Approaches Dejing Dou Hao Wang Haishan Liu Computer and Information Science Computer and Information Science Computer and Information Science University of Oregon University of Oregon University of Oregon Eugene, OR 97403, USA Eugene, OR 97403, USA Eugene, OR 97403, USA Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Semantic Data Mining refers to the data mining tasks domain knowledge can work as a set of prior knowledge of that systematically incorporate domain knowledge, especially for- constraints to help reduce search space and guide the search mal semantics, into the process. In the past, many research efforts path [8], [9]. Further more, the discovered patterns can be have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge cleaned out [49], [48] or made more visible by encoding them engineering has enriched the family of domain knowledge, espe- in the formal structure of knowledge engineering [76]. cially formal semantics and Semantic Web ontologies. Ontology is To make use of domain knowledge in the data mining pro- an explicit specification of conceptualization and a formal way to cess, the first step must account for representing and building define the semantics of knowledge and data. The formal structure the knowledge by models that the computer can further access of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce and process. The proliferation of knowledge engineering (KE) general concepts of semantic data mining.